VNP13A1 v002

• Improved calibration algorithm and coefficients for entire Suomi NPP mission.
• Improved geolocation accuracy and applied updates to fix outliers around maneuver periods.
• Corrected the aerosol quantity flag (low, average, high) mainly over brighter surfaces in the mid- to high-latitudes such as desert and tropical vegetation areas. This has an impact on the retrieval of other downstream data products such as VNP13 Vegetation Indices and VNP43 Bidirectional Reflectance Distribution Function (BRDF)/Albedo.
• Improved cloud mask input product for corrections along coastlines and artifacts from use of coarse resolution climatology data.
• Replaced the land/water mask input product with the eight-class land/water mask from the VNP03 geolocation product that better aligns with MODIS.
• Modified QA VI Usefulness bits to ignore BRDF flag.
• Implemented VI specific land/water mask.

More details can be found in this VIIRS Land V2 Changes document.

Validation at stage 1 has been achieved for the VIIRS Vegetation Index (VI) product suite. Visit VIIRS Land Product Quality Assessment Product Maturity for information on product maturity status.

All scale factors are to be divided.

Fill Values for the Vegetation Index Layers

Fill Values for the Pixel Reliability Layer

Rankings for the Pixel Reliability Layer

Product Quality information can be found in the combined User Guide and Algorithm Theoretical Basis Document (ATBD). For the “500 m 16 days pixel reliability” SDS Layer, a rank quality table for each pixel can be found in Table 5. For the “500 m 16 days VI Quality” SDS Layer, for use with all VI’s, a bit field Quality Assurance table can be found in Table 13, with additional information available in Table 14. Accuracy Precision and Uncertainty information is available in Section 12.1.4.

The Quality Assessment (QA) layer is stored in an efficient bit-encoded manner. The unpack_sds_bits executable from the LDOPE Tools is available to the user community to help parse and interpret the quality layer.

Quality assurance information should be considered when determining the usability of data for a particular science application. The ArcGIS MODIS-VIIRS Python Toolbox contains tools capable of decoding quality data layers while producing thematic quality raster files for each quality attribute.

For additional information on product quality, refer to the VIIRS Land Product Quality website.

For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website and the User Guide and ATBD.